You couldn’t miss it. Every billboard on the way into San Francisco. Every booth on the show floor. Every keynote, panel, and happy hour conversation. AI was everywhere at RSA this year, supporting the theme “Many Voices. One Community,” in spades.
What was also everywhere, if you were listening carefully: skepticism.
Buyers are curious about AI. They are not, for the most part, ready to hand it the keys. After spending time on the floor and in conversations with analysts and practitioners, the tension between the promise of AI and the reality of deploying it responsibly was the most honest and useful theme of the entire conference.
The signal underneath all the noise
Strip away the marketing (which was once again big!), and a clear picture emerges of where AI is actually delivering value in cybersecurity today. The highest-trust use cases are pattern recognition, noise reduction, enrichment, and summarization. These are the areas where AI can meaningfully accelerate what skilled teams are already doing, without replacing the judgment behind it.
The lower-trust use cases such as autonomous remediation and automated patching, remain a bridge too far for most organizations. Not because the technology isn’t advancing, rather because the data foundations and guardrails required to deploy it responsibly aren’t yet in place at most enterprises.
Speaking with our analyst partners, it is clear that this is a realistic assessment of where the market actually is.
Explainability is the real opportunity
One of the most important distinctions that came through our conversations at RSA: the greatest near-term value from AI isn’t autonomous action, it’s explainability and translation.
Security teams need AI that can communicate the why behind an exposure. That can explain the impact of a change request to a network engineer in language that makes sense to a network engineer and then also frame the same issue for a C-suite executive in terms of business risk. That translation layer is where AI earns its credibility, and it’s where most vendors are still falling short.
At RedSeal, this is exactly the capability we’ve been building toward. Our AI-powered explainability work is designed to meet teams where they already work, in their own channel and in natural language so that exposure context becomes part of the workflow, not a separate reporting exercise.
Trust is the real barrier to AI adoption
Here’s what the vendor community needs to hear more clearly: buyers don’t distrust AI explanations. They distrust authority inversion.
Trust breaks down when recommendations feel constructed after the fact, or when the rationale behind a decision feels persona-driven rather than evidence-driven. The implication is significant, AI credibility is built on the strength of the underlying exposure data. Get the data right first, then layer in AI as a controlled accelerator.
The sequencing that kept surfacing in analyst conversations was consistent: build and maintain foundational exposure datasets, integrate exposure context into SOC workflows, and introduce agentic AI only after data integrity and guardrails are established. A disciplined approach that will drive your pre-emptive exposure management practices.
Differentiate on outcomes, not AI claims
With 70 to 80-plus vendors making nearly identical AI claims on the show floor, the path to standing out isn’t a better AI story. It’s demonstrable outcomes including shorter exposure windows, faster resolution, and clearer priorities. The trust that gets built between security and network teams when tickets carry rationale and bridge the language gap are those that drive efficiency.
Worth naming directly: investor pressure to AI-wash messaging is real, and buyers are increasingly aware of it. Failed POVs from well-funded vendors are already making the rounds. Skepticism is growing, and it will only reward organizations that lead with substance.
What this means for security leaders
RSA this year was a useful forcing function. AI in cybersecurity is not a future consideration but a present-tense decision that requires a strategy, and fast. That strategy starts with data integrity, not AI features, and requires building the translation layer between technical exposure and business risk while resisting the pressure to chase autonomous capabilities before the foundational work is done.
RedSeal has spent years building the visibility, analysis, and validation that makes that foundation possible. AI makes it faster, clearer, and more actionable for every team that needs to act on it, from network engineers to the board.
Contact us today to learn more about our architecture-driven foundation powered by AI and Agentic capabilities, and how RedSeal can help you to move from insight to action with confidence.
